Forecasting House Prices in the 50 States Using Dynamic Model Averaging and Dynamic Model Selection PDF Download
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Author: Lasse Bork Publisher: ISBN: Category : Languages : en Pages : 44
Book Description
We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantially. The states in which housing markets have been the most volatile are the states where model change and parameter shifts have been the most needed.
Author: Lasse Bork Publisher: ISBN: Category : Languages : en Pages : 44
Book Description
We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantially. The states in which housing markets have been the most volatile are the states where model change and parameter shifts have been the most needed.
Author: Raffaele Argiento Publisher: Springer ISBN: 331954084X Category : Mathematics Languages : en Pages : 242
Book Description
This book is a selection of peer-reviewed contributions presented at the third Bayesian Young Statisticians Meeting, BAYSM 2016, Florence, Italy, June 19-21. The meeting provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and postdocs dealing with Bayesian statistics to connect with the Bayesian community at large, to exchange ideas, and to network with others working in the same field. The contributions develop and apply Bayesian methods in a variety of fields, ranging from the traditional (e.g., biostatistics and reliability) to the most innovative ones (e.g., big data and networks).
Author: Yarui Li Publisher: ISBN: Category : Languages : en Pages :
Book Description
The housing market plays a significant role in shaping the economic and social well-being of U.S. households. It helps spur U.S. economic growth when house prices rise, and drags the economic growth when house prices drop. In this dissertation, an analysis is conducted to project the direction of the U.S. housing market and to discover how it interacts with economic fundamentals. New pieces of information are found, which are deemed to facilitate decision making for both policy makers and investors. In the first part of the dissertation, the groupings of U.S. housing markets are studied using cluster and discriminant analysis. Three clusters are found, which are located in the central, the east coast, and the west coast of US. There are no price signals transmitted among these housing market clusters, nor within each cluster. Thus, the communication of information in the housing market is through the process of utility convergence of marginal residents, and no price convergence across regions is found. Next, the impact of credit constraint on the house prices is examined with the stochastic components of the price series being considered. Both a simulation technique and a DAG approach are employed. The resulting causal pattern shows that credit constraints affect the house prices directly and positively. Moreover, credit constraints work as an intermediary, passing the influence of the house investor, household income, and user cost onto house prices, which suggests that the credit relaxation policy should be carried out with caution when house inventory and household income send inconsistent signals. Last, the model selection for house price analysis is discussed from the perspective of large-scale models -- dynamic factor (DFM) model and large-scale Bayesian VAR (LBVAR) model. The LBVAR models are found to have superior performance compared to the DFM model throughout the prediction period. Also, it is found that the combined forecasts do not necessarily outperform individual forecasts. Even though independent information from different individual models improves the forecast accuracy, the benefit gained from marginal information is offset by the larger error brought by such combination. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151894
Author: Enver Ever Publisher: Springer Nature ISBN: 3030694313 Category : Computers Languages : en Pages : 187
Book Description
This proceedings constitutes the refereed proceedings of the First EAI International Conference on Forthcoming Networks and Sustainability in the IoT Era, FoNeS 2020, held in October 2020. Due to COVID-19 pandemic the conference was held virtually. The 13 papers presented were carefully selected from 28 submissions. The papers focus application areas for advanced communication systems and development of new services, in an attempt to facilitate the tremendous growth of new devices and smart things that need to be connected to the Internet through a variety of wireless technologies. The papers are organized in topical sections on IoT and network applications; machine learning and distributed computing; and cellular networks and security.
Author: Marko Hannonen Publisher: Suomen E-painos Oy ISBN: 9526613767 Category : Mathematics Languages : en Pages : 51
Book Description
This booklet discusses some major methodological issues relating to the construction of house price models on a macro level. There is no single method that always produces the optimal results; the choice of a particular approach, method, theory, model and technique is context-dependent. This is especially true in housing markets, where a multitude of different submarkets exist. The methodology chosen should be based on sound theory, from which the basic concepts of analysis can be derived. This booklet discusses the use of potential models, which can be constructed using a general field theory, and which act as a theoretical foundation for further analysis. If we use potential models for house price analysis we can discover additional features from the data set that other approaches would simply miss. This e-book presents a pragmatic overview of key methodological concerns with the emphasis on the use of potential models. Theoretical methodological questions are left unanswered, and are not even presented in this text, since they have little relevancy to real-world modelling questions.
Author: Graham Elliott Publisher: Elsevier ISBN: 0444627413 Category : Business & Economics Languages : en Pages : 1386
Book Description
The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. - Focuses on innovation in economic forecasting via industry applications - Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications - Makes details about economic forecasting accessible to scholars in fields outside economics
Author: Sean Holly Publisher: ISBN: Category : Languages : en Pages : 31
Book Description
The purpose of this paper is to apply recent advances in the econometrics of panel data to a problem that has a clear spatial dimension. We model the dynamic adjustment of real house prices using data at the level of US States. In the last decade, in most OECD countries there has been a significant rise in real house prices. This attracted the attention of many international organisations and central banks. In this paper we consider interactions between housing markets by examining the extent to which real house prices at the State level are driven by fundamentals such as real income, as well as by common shocks, and determine the speed of adjustment of house prices to macroeconomic and local disturbances. We take explicit account of both cross sectional dependence and heterogeneity. This allows us to find a cointegrating relationship between house prices and incomes and to identify a small role for real interest rates. Using this model we then examine the role of spatial factors, in particular the effect of contiguous states by use of a weighting matrix. We are able to identify a significant spatial effect, even after controlling for State specific real incomes, and allowing for a number of unobserved common factors.
Author: Alex Van de Minne Publisher: ISBN: Category : Languages : en Pages : 36
Book Description
The general purpose of a dynamic factor model (DFM) is to summarize a large number of time series into a few common factors. Here we explore a number of DFM specifications applied to 80 granular, non-overlapping indexes of commercial property prices in the US, quarterly from 2001 to 2017. We examine the nature and the structure of the factors and the index forecasts that can be produced using the DFMs. We consider specifications of 1, 2, 3 and 4 common factor trends. As a major motivation for the use of DFMs is their ability to improve out-of-sample forecasting of systems of numerous related series, we apply the DFM estimated factor returns in an Autoregressive Distributed Lag (ARDL) model to forecast the individual real estate price series. We compare the forecasted residuals to a conventional Autoregressive (AR) forecast model as a "benchmark" for two markets: Boston apartments and Dallas commercial. The results show that the ARDL model predicts the crisis and subsequent recovery really well, whereas the "benchmark" model typically follows the previous price trend. We find that the DFM forecasts are most precise with only one or two factors. The two prominent factors may reflect general economic conditions and the rental housing market, respectively.
Author: Lasse Bork
Author: Lasse Bork
Author: Xiaolong Li
Author: Raffaele Argiento
Author: Yarui Li
Author: Enver Ever
Author: Marko Hannonen
Author: Graham Elliott
Author: Sean Holly
Author: Marina Emiris
Author: Alex Van de Minne